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Fingerprint Recognition System Using Hybrid Matching Techniques

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Fingerprint Recognition System Using Hybrid Matching Techniques

Introduction

There are two types of systems that help to automatically establish the identity of a person: (a) authentication(verification) systems, and (b) identification systems.

In a verification (authentication) system, a person desired to be identified submits a claim to an identity to the system, usually via a magnetic stripe card, login name, smart card etc., and the system either rejects or accepts the submitted claim of identity.

In an identification system, the system establishes a subject’s identity (or fails if the subject is not enrolled in the system database) without the subject having to claim an identity.

Introduction

Fingerprint matching techniques can be broadly divided in two categories, minutiae-based and correlation-based.

Minutiae-based techniques attempt to align two sets of minutiae points from two fingerprints and count the total number of matched minutia.

In the correlation-based approach, global patterns of ridges and furrows are compared to determine if the two fingerprints align.

Any human physiological or behavioral characteristic can be used as a biometric characteristic for person identification as long as it satisfies the following requirements:

(a) universality

(b) uniqueness

(c) permanence

(d) collectability

The Structure of the Proposed Hybrid System

The proposed system is represented briefly in the block diagram shown in figure . The system is based mainly on two techniques. The first one adopts the minutia algorithm and the second adopts the ridge algorithm.

Feature Extraction with Minutiae Algorithm

Algorithm: This algorithm tests the validity of each minutiae point by scanning the skeleton image and examining the local neighborhood around the point.

The subsequent steps of the algorithm depend on whether the candidate minutiae point is a ridge ending or a bifurcation.

1. For a candidate ridge ending point: If T01 = 1, then the candidate minutiae point is validated as a true ridge ending.

Ridge Matching

1.Aligning Query and Template Images:

For comparing the ridge feature maps of two images, it is necessary that the images themselves are aligned appropriately to ensure an overlap of common region in the two fingerprint images. This is done by determining the transformation parameters, (tx, ty, tφ ).

Ridge Matching

The ridge feature maps of the query and the template images are compared by computing the sum of the Euclidean distances of the 8-dimensional feature vectors in the corresponding tessellated cells.

Cells that are marked as background are not used in the matching process.

This results in a distance score measure; a higher distance score indicates a poor match.

Combining Matching Scores

The matching scores generated by comparing the minutiae sets and the ridge feature maps are combined to generate a single matching score.

There are three cases to generate a single matching score:

1. If the verification system detects a fingerprint image more than or equal

to the threshold and the identification system detects the same

fingerprint image we adopt the following sum rule.

2. If the verification system detects a fingerprint image less than the

threshold and the identification system detects the same fingerprint

image we adopt the same sum rule equation.

3. If the verification system detects fingerprint images more than or equal

to the threshold and the identification system did not detect the same

fingerprint image we use the following equation.

Experimental Results

This research uses two databases to test a fingerprint matching system.

1. individual database

2. identical twins database

Experimental results are obtained for the following three algorithms:

1. Proposed verification matching which used two algorithms in

the post-process phase, Xiao et al and Tico algorithms,

2. Central point identification matching and

3. Hybrid matching which is a combination of previous two algorithms.

All the above three algorithms are experimented using Individual Data base as well as Identical Twins Database.

Individual Database:

1. Proposed Verification Matching Algorithm:

Table shows the fingerprint verification matching using Xiao and Tico algorithms, separately in individual fingerprint database and after using the proposed combined verification fingerprint matching algorithm, corresponding to different threshold values.

2. The Central Point Identification Matching Algorithm:

Acceptance rate 86.5 % (independent of threshold values).

3. Hybrid Matching Algorithm:

Hybrid between two previous matching algorithms results in different thresholds. They are: 0.15, 0.2, 0.25 and 0.3. The corresponding matching rates are 99.3%, 99.3%, 97.9%, and 95.9% respectively.

Identical Twins Database:

1. Proposed Verification Matching Algorithm:-

Table shows the results for Xiao and Tico algorithms and the proposed combined verification algorithm, for the identical twins algorithm.

2. Central Point Identification Matching Algorithm:

Result of using central point identification matching algorithm. Acceptance rate: 87.7 % (independent of threshold values).

3. Hybrid Matching Algorithm:

The matching results were conducted by using hybrid matching in identical twins at different thresholds. Thresholds (0.25, 0.3, 0.35, and 0.4) it is matching results in Hybrid matching system are (100%, 100%, 98.5%, and 98.5%) respectively.

There are some problems in collecting the second database:

1. The different age of the persons leads to a different size of the

fingerprint

2. Some of the twins are children so there are scratches in the

fingerprints

3. Some of them did not fully cooperate with the researchers, so

most of the images of their fingerprints do not contain enough

features to create an extraction.

Conclusion

This research introduces an Automatic Fingerprint Recognition System (AFRS) based on hybrid techniques for matching.

Experiments indicated that the hybrid technique performs much better than each algorithm individually.